This contributed volume aims to build the foundation of a framework for computationally aware algorithmic design for cyber-physical systems (CPSs), focusing on approaches that take computation into account at the design stage to address their impact on performance and safety. It demonstrates how novel techniques may emerge from the combination of formal methods, model predictive control, distributed optimization, data-driven methods, reconfigurable adaptive methods, and information-theoretic techniques.

Chapters are written by both researchers and practitioners and cover such topics as
analysis and design of uncertain CPSs,
cooperative and non-cooperative paradigms for handling complexity in large scale CPSs,
task-relevant environment abstractions for autonomous systems based on information theory,
information flow in event-based stabilization of CPSs,
set-valued model predictive control, and
automated synthesis of certifiable controllers for CPSs.

State-of-the-art applications and case studies are provided throughout with a special focus on intelligent transportation systems and autonomous vehicles.

Graduate students and researchers with an interest in CPS verification and control will find this volume to be a valuable resource in their work. It will also appeal to researchers from disciplines other than control, such as computer science, operations research, applied mathematics, and robotics.